Wireless sensor networks have the potential for diverse application for example, in building surveillance, disaster prevention and environmental monitoring. A wireless sensor network is made up of a relatively large number of nodes, generally referred to as network nodes, that are located in and spread over a geographic area corresponding to that in which the wireless sensor network is implemented. The network nodes may typically be inexpensive, battery-powered, electronic devices, such as sensors, for example, with reduced capability to store, process and/or analyse data. The network nodes are generally applied to fulfill at least two challenging tasks: firstly, to continuously monitor the status of a physical phenomena or environmental condition(s), and secondly, transmitting the collected data to a remote central server for the processing and/or analysis of the data. In order to facilitate the latter task, each of the network nodes may be equipped with a relatively low-power radio transceiver with reduced range coverage. Cooperation between the radio transceivers corresponding with the network nodes is used to form a wireless ad hoc network for routing data sensed by one of the network nodes, generally referred to as the source node, to the remote central server, generally referred to as a destination node, through selected network nodes between the source node and the destination node via multi-hop transmission, such selected network nodes being hereinafter referred to as forwarding nodes.
A problem of wireless sensor networks, particularly when the network nodes are implemented by way of electronic devices powered by battery, is that loss of battery in one or more of the network nodes may cause data loss and, in the worst case, a complete failure, in the operation of the wireless sensor network.
In order to address the issue of battery conservation in the network nodes, it has been previously-proposed to cyclically operate a wireless sensor network in one of two modes of operation: a sleep mode and an active mode. In the sleep mode of operation, the radio transceivers of the network nodes are switched off and so, in this mode of operation, the network nodes do not contribute in the forwarding of data. In the active mode of operation, neighbouring network nodes, and specifically radio transceivers corresponding thereto, communicate with each other and are involved in the forwarding of data in the direction of and, ultimately, to the destination node. In order to facilitate such communication, in the active mode, those network nodes that are involved in forwarding the data towards the destination node are synchronised such that the scheduling of data receipt and/or transmission between them is conducted in such a way that a scenario of data loss is reduced or altogether avoided. In this regard, IEEE Standard, “Part 15.4: Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low-Rate Wireless Personal Area Networks”, IEEE, Los Alamos, 2003, describes a synchronisation strategy for use in a wireless network that is implemented by periodically transmitting beacons from a central network coordinator to the network nodes. After beacon reception, the radio transceiver of a synchronised network node listens to the radio channel established between the radio transceivers of the network nodes for a time-period of such a duration that data packets may be received from neighbouring network nodes and, if needed, transmitted onwards towards the destination node. After completion of these tasks, and until the next beacon is received, the aforementioned synchronised network node is operated in the sleep mode, with its associated radio transceiver being powered off. The performance gain obtained with this power management strategy is governed by the duty cycle of the network node, which is defined as the ratio of the length of the active period to one beacon interval. Other similar strategies have been described in J. H. Kim et. al., “Power saving method for wireless sensor network”, U.S. Pat. No. 7,447,256, November 2008, and C. J. Yoon, “Energy-efficient medium access control protocol and system for sensor networks”, US Patent Application 2006/0128349, June 2006.
In previously-proposed wireless sensor networks, static routing protocols have been used to propagate data packets from the source node to the destination node. Examples of such static routing protocols have been described by C. Perkins and E. Royer, in the document titled, “Ad-hoc on demand distance vector (AodV) routing” published in IEEE WMSCA, New Orleans, La., US, February 1999 and by D. Johnson, D. Maltz and J. Broch, in the document titled, “DSR: The dynamic source routing protocol for multi-hop wireless and ad-hoc networks”, published in Ad-Hoc Networking, Addison Wesley, 2001. Such static routing protocols rely on the establishment of a single path from the source node to the destination node before the transmission of data by the source node to those network nodes determined in accordance with the static routing protocol for forwarding data to the destination node. Due to the execution of route discovery and route maintenance procedures being done before transmission of the data from the source node, some drawbacks associated with static routing protocols include an increased protocol overhead and performance degradation in terms of energy consumption. Furthermore, since the protocol overhead generally exponentially increases with the number of network nodes, static routing protocols may not provide an energy-efficient solution for relatively large-scale wireless sensor networks.
For large-scale wireless sensor networks, it has been proposed to use a geographic routing protocol. An example of a geographic routing protocol has been provided by M. Zorzi and R. R. Rao in the document titled, “Geographic random forwarding (GeRaF) for ad-hoc sensor networks: multi-hop performance”, published in IEEE Transactions on Mobile Computing, pp. 337-348, 2003. Geographic routing is based on the principle that a route for forwarding data from the source node to the destination node is dynamically constructed whilst data is being transmitted from the source node in the direction of, and to, the destination node via forwarding nodes. The aforementioned dynamic construction of the route is performed on the basis of information on the geographic location information of the involved forwarding nodes. In geographic routing, a node that has a data packet to transmit broadcasts a request message containing information on the geographical coordinates of the destination node. Network nodes within the radio coverage range of the broadcasting node each receive the request message, such network nodes being generally referred to as neighbouring network nodes. They then exploit their topological knowledge to calculate the advancement that they can offer towards the destination node and contend amongst each other to elect the next network node that is closest to the destination node. Since information is only locally exchanged between a node that has data to transmit and neighbouring network nodes, geographic routing scales with the size of the wireless sensor network in which it is implemented, thereby making it advantageous for use in large-scale wireless sensor networks compared to previously-proposed routing protocols, such as, for example, static routing protocols. A prerequisite for the application of geographic routing is that all network nodes know their own geographic location within the network. This geographic information may be: set manually at initial deployment of the network nodes; or be provided using a location positioning system such as a global positioning system (GPS) in an outdoor environment, or by using the positioning system proposed by S. Furrer, W. Schott and B. Weiss in U.S. Pat. No. 7,761,233, July 2010, for example.
Whilst geographic routing protocols alleviate some of the drawbacks associated with previously-proposed techniques/protocols, it is still a challenge to route data in a wireless sensor network, particularly one that is large-scaled, with improved reliability and network performance whilst also addressing energy consumption issues.